# How to Calculate Mean Absolute Percentage Error (MAPE) in Excel

One of the most common metrics used to measure the forecasting accuracy of a model is MAPE, which stands for mean absolute percentage error

The formula to calculate MAPE is as follows:

MAPE = (1/n) * Σ(|actual – forecast| / |actual|) * 100

where:

• Σ – a fancy symbol that means “sum”
• n – sample size
• actual – the actual data value
• forecast – the forecasted data value

MAPE is commonly used because it’s easy to interpret and easy to explain. For example, a MAPE value of 11.5% means that the average difference between the forecasted value and the actual value is 11.5%.

The lower the value for MAPE, the better a model is able to forecast values. For example, a model with a MAPE of 2% is more accurate than a model with a MAPE of 10%.

## How to Calculate MAPE in Excel

To calculate MAPE in Excel, we can perform the following steps:

Step 1: Enter the actual values and forecasted values in two separate columns. Step 2: Calculate the absolute percent error for each row.

Recall that the absolute percent error is calculated as: |actual-forecast| / |actual| * 100. We will use this formula to calculate the absolute percent error for each row.

Column D displays the absolute percent error and Column E shows the formula we used: We will repeat this formula for each row: Step 3: Calculate the mean absolute percent error.

Calculate MAPE by simply finding the average of the values in column D: The MAPE of this model turns out to be 6.47%.

### A Note On Using MAPE

Although MAPE is straightforward to calculate and easy to interpret, there are a couple potential drawbacks to using it:

1. Since the formula to calculate absolute percent error is |actual-forecast| / |actual| this means that it will be undefined if any of the actual values are zero.

2. MAPE should not be used with low volume data. For example, if the actual demand for some item is 2 and the forecast is 1, the value for the absolute percent error will be |2-1| / |2| = 50%, which makes it seem like the forecast error is quite high, despite the forecast only being off by one unit.

Another common way to measure the forecasting accuracy of a model is MAD – mean absolute deviation. Read about how to calculate MAD in Excel here.